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loading_zipdata <- "~/repos/Diversity-Richness/Zip.Code.Datasets/zip_data_unedited_nolabels.csv" #making a pathway to the downloaded census data
Unedited_zipdata <- read.csv(loading_zipdata) #loading the census data
reduced_collumns_zip <- c(1:2, 33:34, 42, 47:146) #removing unnecessary columns
Zipcode_Census_data<- Unedited_zipdata[ , reduced_collumns_zip] #make a new table without unnecessary columns
colnames(Zipcode_Census_data) <- c("FIPS", "Name", "3_Digit_Tabulation", "5_Digit_Tabulation" , "Area_Name","Total_Population", "Population_Density", "Area", "Total_Population1", "Total_Population_Male", "Total_Population_Female", "X_Total_Population_Male", "X_Total_Population_Female", "Total_Population2", "Total_Population_Under_5_Years", "Total_Population_5_to_9_Years", "Total_Population_10_to_14_Years", "Total_Population_15_to_17_Years", "Total_Population_18_to_24_Years", "Total_Population_25_to_34_Years", "Total_Population_35_to_44_Years", "Total_Population_45_to_54_Years", "Total_Population_55_to_64_Years", "Total_Population_65_to_74_Years", "Total_Population_75_to_84_Years", "Total_Population_85_Years_And_Over","X_Total_Population_Under_5_Years", "X_Total_Population_5_to_9_Years", "X_Total_Population_10_to_14_Years", "X_Total_Population_15_to_17_Years", "X_Total_Population_18_to_24_Years", "X_Total_Population_25_to_34_Years", "X_Total_Population_35_to_44_Years", "X_Total_Population_45_to_54_Years", "X_Total_Population_55_to_64_Years", "X_Total_Population_65_to_74_Years", "X_Total_Population_75_to_84_Years", "X_Total_Population_85_Years_And_Over", "Total_Population3", "Total_Population_White_Alone", "Total_Population_Black_or African_American_Alone", "Total_Population_American_Indian_And_Native_Alaskan_Alone", "Total_Population_Asian_Alone", "Total_Population_Native_Hawaiian_And_Other_Pacific_Islander_Alone", "Total_Population_Some_Other_Race_Alone","Total_Population_Two_Or_More_Races","X_Total_Population_White_Alone", "X_Total_Population_Black_or_African_American_Alone", "X_Total_Population_American_Indian_And_Native_Alaskan_Alone", "X_Total_Population_Asian_Alone", "X_Total_Population_Native_Hawaiian_And_Other_Pacific_Islander_Alone", "X_Total_Population_Some_Other_Race_Alone","X_Total_Population_Two_Or_More_Races","Households","Households_Family_Households","Households_Married_Couple_Family","Households_Other_Family","Households_Male_Householder_No_Wife_Present","Households_Female_Householder_No_Husband_Present","Households_Nonfamily_Households","Nonfamily_Households_Male_Householder","Nonfamily_Households_Female_Householder","X_Households_Family_Households","X_Households_Married_Couple_Family","X_Households_Other_Family","X_Households_Male_Householder_No_Wife_Present","X_Households_Female_Householder_No_Husband_Present","X_Households_Nonfamily_Households","X_Nonfamily_Households_Male_Householder","X_Nonfamily_Households_Female_Householder","Median_Household_Income","Housing_Units","Occupied_Housing_Units","Owner_Occupied","Renter_Occupied","X_Owner_Occupied","X_Renter_Occupied","Population_With_Poverty_Status","Poverty_Status_Under_1.00","Poverty_Status_1.00_to_1.99","Poverty_Status_Under_2.00","Poverty_Status_2.00_and_Over","X_Poverty_Status_Under_1.00","X_Poverty_Status_1.00_to_1.99","X_Poverty_Status_Under_2.00","X_Poverty_Status_2.00_and_Over", "Total_Population_Last", "Total_Not_Hispanic", "Total_Not_Hispanic_White_Alone", "Total_Not_Hispanic_Black_Alone", "Total_Not_Hispanic_American_Indian_Native_Alaskan_Alone","Total_Not_Hispanic_Asian_Alone","Total_Not_Hispanic_Native_Hawaiian_Pacific_Islander","Total_Not_Hispanic_Other_Race","Total_Not_Hispanic_Two_or_More_Races", "Total_Hispanic","X_Not_Hispanic", "X_Not_Hispanic_White_Alone", "X_Not_Hispanic_Black_Alone", "X_Not_Hispanic_American_Indian_Native_Alaskan_Alone","X_Not_Hispanic_Asian_Alone","X_Not_Hispanic_Native_Hawaiian_Pacific_Islander","X_Not_Hispanic_Other_Race","X_Not_Hispanic_Two_or_More_Races", "X_Hispanic")
#writing usable column names
write.csv(Zipcode_Census_data, file = "~/repos/Diversity-Richness/Zip.Code.Datasets/Zipcode_Census_data.csv", row.names = FALSE)
#saving the table as a csv file
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
values <- c("Percent White" = "red","Percent African American" = "blue", "Percent Asian" = "green", "Percent Native American" = "purple", "Percent Hispanic" = "yellow")
text_needed <-
"Percent White = RED
Percent African American = BLUE
Percent Asian = GREEN
Percent Native American = PURPLE
Percent Hispanic = YELLOW"
Zipcode_Census_data %>%
group_by()